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Research Papers

An Unsupervised Machine Learning Approach to Assessing Designer Performance During Physical Prototyping

[+] Author and Article Information
Matthew L. Dering

Computer Science and Engineering,
Pennsylvania State University,
University Park, PA 16801
e-mail: dering@cse.psu.edu

Conrad S. Tucker

Industrial Engineering,
Pennsylvania State University,
University Park, PA 16801
e-mail: ctucker4@psu.edu

Soundar Kumara

Industrial Engineering,
Pennsylvania State University,
University Park, PA 16801
e-mail: u1o@engr.psu.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received September 1, 2016; final manuscript received July 12, 2017; published online November 13, 2017. Assoc. Editor: Monica Bordegoni.

J. Comput. Inf. Sci. Eng 18(1), 011002 (Nov 13, 2017) (10 pages) Paper No: JCISE-16-2066; doi: 10.1115/1.4037434 History: Received September 01, 2016; Revised July 12, 2017

An important part of the engineering design process is prototyping, where designers build and test their designs. This process is typically iterative, time consuming, and manual in nature. For a given task, there are multiple objects that can be used, each with different time units associated with accomplishing the task. Current methods for reducing time spent during the prototyping process have focused primarily on optimizing designer to designer interactions, as opposed to designer to tool interactions. Advancements in commercially available sensing systems (e.g., the Kinect) and machine learning algorithms have opened the pathway toward real-time observation of designer's behavior in engineering workspaces during prototype construction. Toward this end, this work hypothesizes that an object O being used for task i is distinguishable from object O being used for task j, where i is the correct task and j is the incorrect task. The contributions of this work are: (i) the ability to recognize these objects in a free roaming engineering workshop environment and (ii) the ability to distinguish between the correct and incorrect use of objects used during a prototyping task. By distinguishing the difference between correct and incorrect uses, incorrect behavior (which often results in wasted time and materials) can be detected and quickly corrected. The method presented in this work learns as designers use objects, and infers the proper way to use them during prototyping. In order to demonstrate the effectiveness of the proposed method, a case study is presented in which participants in an engineering design workshop are asked to perform correct and incorrect tasks with a tool. The participants' movements are analyzed by an unsupervised clustering algorithm to determine if there is a statistical difference between tasks being performed correctly and incorrectly. Clusters which are a plurality incorrect are found to be significantly distinct for each node considered by the method, each with p ≪ 0.001.

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Figures

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Fig. 1

An outline of the method presented in this work

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Fig. 2

Top: the RGB image data gathered from a scene (in RGB order); bottom: the depth information given by the sensor (in millimeters)

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Fig. 3

A student beginning the “hammer screw” task in the engineering lab. For the two types of tasks (hammering a screw and a nail), this method determines if the tool they are using is efficient.

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Fig. 4

The transformed skeletons of the shortest and tallest participants in the study

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Fig. 5

Cluster residency by activity label

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Fig. 6

Distribution plots for inlying and outlying distances

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